There remains a clinical need for better approaches to rapid drug susceptibility testing in view of the increasing burden of multidrug resistant tuberculosis. Binary susceptibility phenotypes only capture changes in minimum inhibitory concentration when these cross the critical concentration, even though other changes may be clinically relevant. We developed a machine learning system to predict minimum inhibitory concentration from unassembled whole-genome sequencing data for 13 anti-tuberculosis drugs. We trained, validated and tested the system on 10,859 isolates from the CRyPTIC dataset. Essential agreement rates (predicted MIC within one doubling dilution of observed MIC) were above 92% for first-line drugs, 91% for fluoroquinolones and aminoglycosides, and 90% for new and repurposed drugs, albeit with a significant drop in performance for the very few phenotypically resistant isolates in the latter group. To further validate the model in the absence of external MIC datasets, we predicted MIC and converted values to binary for an external set of 15,239 isolates with binary phenotypes, and compare their performance against a previously validated mutation catalogue, the expected performance of existing molecular assays, and World Health Organization Target Product Profiles. The sensitivity of the model on the external dataset was greater than 90% for all drugs except ethionamide, clofazimine and linezolid. Specificity was greater than 95% for all drugs except ethambutol, ethionamide, bedaquiline, delamanid and clofazimine. The proposed system can provide quantitative susceptibility phenotyping to help guide antimicrobial therapy, although further data collection and validation are required before machine learning can be used clinically for all drugs.

中文翻译：

---

使用未组装的测序数据和机器学习对结核分枝杆菌进行定量药物敏感性检测

鉴于耐多药结核病的负担不断增加，临床上仍然需要更好的方法来快速进行药物敏感性测试。二元易感性表型仅捕获超过临界浓度时最小抑制浓度的变化，即使其他变化可能与临床相关。我们开发了一个机器学习系统来预测 13 种抗结核药物未组装的全基因组测序数据的最低抑制浓度。我们在来自 CRyPTIC 数据集的 10,859 个分离物上训练、验证和测试了系统。一线药物的基本一致率（预测的 MIC 在观察到的 MIC 的两倍稀释内）高于 92%，氟喹诺酮类和氨基糖苷类药物为 91%，新药和再利用药物为 90%，尽管后一组中极少数表型耐药菌株的性能显着下降。为了在没有外部 MIC 数据集的情况下进一步验证模型，我们预测了 MIC 并将值转换为二进制表型的 15,239 个外部分离株的二进制表型，并将它们的性能与先前验证的突变目录（现有分子检测的预期性能）进行比较和世界卫生组织目标产品概况。对于除乙硫异烟胺、氯法齐明和利奈唑胺之外的所有药物，该模型对外部数据集的敏感性大于 90%。除乙胺丁醇、乙硫异烟胺、贝达喹啉、地拉马尼和氯法齐明外，所有药物的特异性均大于 95%。拟议的系统可以提供定量的易感性表型分析，以帮助指导抗菌治疗，